Notes on Portfolio Theory
Consider an investment portfolio on two assets:
We can calculate the mean and variance of the return on the portfolio, based on the mean and variance of the return on each asset.
We can see that if , we have
diversification, where is linear in the portfolio allocation while the standard deviation is
When , the portfolio variance can be as small as desired. If we set then and the portfolio becomes riskless.
In this following chart we can see a two-asset portfolio return volatility plotted against different weights and correlation.
We see that to as long as the correlation is not perfect between the two assets, there exist a weight that would minimize the portfolio variance (not taking portfolio return into consideration, yet)
Consider assets with return volatility and covariance . Let denote the allocation to asset . Then the variance of portfolio return becomes:
In the case of an equally-weighted portfolio with .
We conclude that in a
equally weighted portfolio (or a diversified portfolio where ) with a
large number of assets,
- the individual asset return variances become unimportant to the portfolio return variance
- the portfolio variance instead now depends on the
average covariancesbetween the assets.
Here, the average return covariance is the
systematic risk that cannot be eliminated through diversification, whereas the average return volatility is the
idiosyncratic risk that is diversifiable.
Note that complete diversification () is achieved when:
- and for a multi-asset portfolio
- for a two-asset portfolio
In a mean-variance space, the set of all possible portfolio with assets form a
convex set. The bounday of this set is known as the
mean-variance frontier and forms a parabola.
The top half of the MV frontier is the set of
efficient MV portfolios which maximize mean return given return variance.
Let us define as the random variable vector of asset returns on assets:
A particular portfolio is defined by the weights assigned to various assets, and we denote the weights vector . The portfolio return is also a random variable, where:
Global Minimum Variance (GMV) portfolio has the
lowest return variance among all possible portfolios, characterized by the leftmost point on the MV frontier. It can be constructed with weight which minimizes the objective function under the constraint of :
Tangent portfolio is a portfolio with the
highest mean/variance ratio among all possible portfolios, characterized by the point that is tangent to the MV frontier and going through the origin:
It turns out that any portfolios on the efficient MV frontier can be constructed based off a linear combination of the GMV and tangent portfolios, which solves the following optimization:
Thus a portfolio is a
MV portfolio if and only if there exists such that:
MV investors that only focus on the mean and variance of a portfolio, then such investors will only hold MV portfolios (which are linear combination of two funds only).
Consider the existence of a risk-free asset with return that has zero variance and correlation with other assets. The
mean excess return is defined as:
And the mean excess return of a portfolio with weight is:
Since the risk-free asset has no variance, the return variance of the portfolio is still
MV portfolio with a risk-free asset is a vector which solves the following optimization:
Note that the constraint that weights sum up to is now dropped, with the inclusion of the risk-free asset.
Thus a portfolio with mean excess return is a
MV portfolio with a risk-free asset if:
This result show that with a risk-free asset, any MV portfolio simply contains a position in the tangency portfolio and a position in the risk-less asset (a.k.a the
Two Fund Separation).
Interesting facts w.r.t. the tangency portfolio :
- is the
uniqueportfolio that is on both the risky and risk-less MV frontiers
- is the point on the
risky MV frontierat which the tangency line goes through point (0, risk-free rate).
We define the
Sharpe ratio (SR) of a portfolio as:
Therefore, the tangency portfolio is the portfolio on the risky MV frontier with the maximum Sharpe ratio.
On the risk-free efficient MV frontier (a.k.a the
Capital Market Line) all portfolio has the same SR the tangency portfolio, since the frontier itself is a straight line.
Sortino ratio improves upon the Sharpe ratio by penalizing the down-side volatility only.
Based on the First Fundamental Theorem of Asset Pricing, given no arbitrage there exists a risk neutral probability measure and a change of measure (R-N derivative) such that for any tradable asset ,
proposition is a linear function of :
Such that all portfolio returns have a
factor-beta representation w.r.t. the tangency portfolio,
By mathematical identity, this will hold in sample exactly.
proof Consider the tangency portfolio from the risk-free MV frontier:
We can show that,
In addition, the covariance can be shown as,
The factor-beta representation is not unique to the tengancy portfolio. In fact, it holds for any arbitraty
We will focus on the tangancy portfolio, W.L.O.G.
The factor-beta representation seems to provde a way to estimate the mean return for any given portfolio. However, it difficult to calculate the tangency weight in practice, due to either circularity (direct estimation) or imprecision (inverting
Linear Factor Model makes an assumption regarding the identify of the tangency portfolio, which avoids the issues stated above.
The LFM assumed tangency portfolio is only used for
the pricing of expected returns. However, additional assumptions can be made regarding investor’s MV preference, such that the assumed tangency portfolio will also be used in actual
The most famous LFM is the
Capital Asset Pricing Model, which assumes a value-weighted market portfolio of all available assets as the tangency portfolio.
The CAPM is a relative pricing formula, which states that the expected return of any asset can be expressed as the sum of the risk-free rate and a portion of the
market risk premium. In other words, it says that the
expected excess return/risk premium of an asset is proportional to the market risk premium. The factor is estimated based on regression. CAPM also assets that market beta is the
only risk associated with higher average returns, and that volatility, skewness, and other covariances do not matter in determining risk premium.
We can also re-write the formula as follow:
This shows that the Sharpe ratio earned on an asset depends only on the correlation between asset return and market returns.
There are two ways to derive CAPM:
- If we assume that
returnsare jointly normal, then the mean and variance are the sufficient statistics for the return distribution, and thus every investor holds a portfolio on the risk-less MV frontier, which is a combination of the tangency portfolio and the risk-free asset. Therefore aggregating across all investors, the market portfolio of all investments is the tangency portfolio.
- If we do not assume jointly normal returns, but instead that investors only care about mean and variance of returns. In this case all investor will also choose MV portfolios, and therefore CAPM holds.
Fama-French 3-factor model is a well-known multi-factor models:
Where is the excess market return as in CAPM, is a portfolio that goes
long small stocks and shorts large stocks, and is a portfolio that goes
long value stocks (low market price per fundamental) and shorts growth stocks.
The FF model states that
beta to value and small stocks earn premium,
being a value or small stock. In other words, the premium is earned on how a stock acts, not how it is classified.
Continue on L3
- FINM 36700 Lecture Notes, Mark Hendricks, the University of Chicago